Object tracking method and system

Abstract

An object tracking method and an object tracking system are provided. The method includes: obtaining a first relative position vector from a first node to a second node at a first time point; obtaining a first position data of the first node and a second position data of the second node at a second time point; identifying a position variation vector from the first node to the second node based on the first position data of the first node and the second position data of the second node; and identifying a second relative position vector from the first node to the second node at the second time point based on the first relative position vector and the position variation vector. The method improves the tracking accuracy.

The invention claimed is: 
     
       1. An object tracking method, comprising:
 obtaining a first relative position vector from a first node to a second node at a first time point via a sensor mounted on the first node; 
 obtaining a first position data of the first node at a second time point via a first Global Positioning System (GPS) device on the first node, and a second position data of the second node at the second time point via a second GPS device on the second node, wherein the first position data and the second position data are GPS raw data; 
 receiving at the first node, via a communication network, the second position data from the second node; 
 identifying a position variation vector from the first node to the second node based on the first position data of the first node and the second position data of the second node; 
 identifying a second relative position vector from the first node to the second node at the second time point based on adding the first relative position vector and the position variation vector; and 
 identifying, prior to obtaining the first relative position vector, the second node, wherein identifying the second node comprises: 
 obtaining a first plurality of features of the second node through one or more sensors mounted on the first node, wherein the first plurality of features comprises at least one of a speed of the second node, a heading of the second node, and a distance between the first node and the second node; 
 receiving at the first node, via the communication network, a message including a second plurality of features of a candidate node, wherein the second plurality of features comprises at least one of a speed of the candidate node, a heading of the candidate node, and a GPS position of the candidate node; and 
 identifying the candidate node as the second node if a similarity index between the first plurality of features and the second plurality of features is larger than a threshold value. 
 
     
     
       2. The method according to  claim 1 , wherein obtaining a first relative position vector from a first node to a second node at a first time point comprises:
 obtaining a third relative position vector from the first node to a third node; 
 obtaining a fourth relative position vector from the third node to the second node; and 
 identifying the first relative position vector from the first node to the second node based on the third relative position vector and the fourth relative position vector. 
 
     
     
       3. The method according to  claim 2 , wherein the third relative position vector is obtained through a sensor mounted on the first node, and the fourth relative position vector is obtained through a sensor mounted on the third node. 
     
     
       4. The method according to  claim 1 , wherein the sensor is a radar, a Lidar or a camera. 
     
     
       5. The method according to  claim 1 , wherein the first position data and the second position data are Global Position System (GPS) raw data. 
     
     
       6. The method according to  claim 5 , wherein the GPS raw data comprises pseudorange, satellite orbital data and carrier phase. 
     
     
       7. The method according to  claim 1 , wherein the first node and the second node communicate through a communication network. 
     
     
       8. The method according to  claim 7 , wherein the communication network is a Dedicated Short Range Communication (DSRC) network, or a cellular network. 
     
     
       9. An object tracking system, comprising a processing device configured to:
 obtain a first relative position vector from a first node to a second node at a first time point, the first relative position vector being obtained through one or more sensors mounted on the first node; 
 obtain a first position data of the first node at a second time point via a first Global Positioning System (GPS) device on the first node; 
 obtain a second position data of the second node at the second time point via a second GPS device on the second node, wherein the first position data and the second position data are GPS raw data; 
 receive at the first node, via a communication network, the second position data from the second node; 
 identify a position variation vector from the first node to the second node based on the first position data of the first node and the second position data of the second node; 
 identify a second relative position vector from the first node to the second node at the second time point based on adding the first relative position vector and the position variation vector; and 
 identify, prior to obtaining the first relative position vector, the second node, wherein identifying the second node comprises: 
 obtaining a first plurality of features of the second node through the one or more sensors mounted on the first node, wherein the first plurality of features comprises at least one of a speed of the second node, a heading of the second node, and a distance between the first node and the second node; 
 receiving at the first node, via the communication network, a message including a second plurality of features of a candidate node, wherein the second plurality of features comprises at least one of a speed of the candidate node, a heading of the candidate node, and a GPS position of the candidate node; and 
 identifying the candidate node as the second node if a similarity index between the first plurality of features and the second plurality of features is larger than a threshold value. 
 
     
     
       10. The system according to  claim 9 , wherein the processing device is further configured to:
 obtain a third relative position vector from the first node to a third node; 
 obtain a fourth relative position vector from the third node to the second node; and 
 identify the first relative position vector from the first node to the second node based on the third relative position vector and the fourth relative position vector. 
 
     
     
       11. The system according to  claim 10 , wherein the third relative position vector is obtained through a sensor mounted on the first node, and the fourth relative position vector is obtained through a sensor mounted on the third node. 
     
     
       12. The system according to  claim 9 , wherein the sensor is a radar, a Lidar or a camera. 
     
     
       13. The system according to  claim 9 , wherein the first position data and the second position data are Global Position System (GPS) raw data. 
     
     
       14. The system according to  claim 13 , wherein the GPS raw data comprises pseudorange, satellite orbital data and carrier phase. 
     
     
       15. The system according to  claim 9 , wherein the first node and the second node communicate through a communication network. 
     
     
       16. The system according to  claim 15 , wherein the communication network is a Dedicated Short Range Communication (DSRC) network, or a cellular network. 
     
     
       17. The method according to  claim 6 , wherein the position variation vector from the first node to the second node is obtained by performing a double differentiation operation on the first position data and the second position data. 
     
     
       18. The system according to  claim 14 , wherein the position variation vector from the first node to the second node is obtained by performing a double differentiation operation on the first position data and the second position data.